On-chip Synthesis of Fine-Tuned Bone-Seeking Hybrid Nanoparticles
Bibliographic record
Abstract
AIMS: Here we report a one-step approach for reproducible synthesis of finely tuned targeting multifunctional hybrid nanoparticles (HNPs). MATERIALS & METHODS: A microfluidic-assisted method was employed for controlled nanoprecipitation of bisphosphonate-conjugated poly(D,L-lactide-co-glycolide) chains, while coencapsulating superparamagnetic iron oxide nanoparticles and the anticancer drug Paclitaxel. RESULTS: Smaller and more compact HNPs with narrower size distribution and higher drug loading were obtained at microfluidic rapid mixing regimen compared with the conventional bulk method. The HNPs were shown to have a strong affinity for hydroxyapatite, as demonstrated in vitro bone-binding assay, which was further supported by molecular dynamics simulation results. In vivo proof of concept study verified the prolonged circulation of targeted microfluidic HNPs. Biodistribution as well as noninvasive bioimaging experiments showed high tumor localization and suppression of targeted HNPs to the bone metastatic tumor. CONCLUSION: The hybrid bone-targeting nanoparticles with adjustable characteristics can be considered as promising nanoplatforms for various theragnostic applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".